Modeling and optimization for proton exchange membrane fuel cell stack using aging and challenging P systems based optimization algorithm

Yang, Shuai; Chellali, Ryad; Lü, Xiaohua; Li, Lijuan; Bo, Cuimei

doi:10.1016/j.energy.2016.04.093

Cited by 48 publications

(20 citation statements)

References 40 publications

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“…Therefore, direct comparison of SSEs between the studies cannot be made. Further, Yang et al [22] reached a low SSE value of 1.1746, but the model structure was altered by adding three more free parameters to the modeling and parameter fitting. The model structure found in this study provides a very low SSE values with the same number of free parameters, and therefore with comparable model complexity than the semi-empirical model structure used in [4,5,6,16,21].…”

Section: Resultsmentioning

confidence: 99%

Genetic Algorithms in Model Structure Identification for Fuel Cell Polarization Curve

Ohenoja

Sorsa

Leiviskä

2018

2018 5th International Conference on Control, Decision and Information Technologies (CoDIT)

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Abstract-Evolutionary optimizers, such as genetic algorithms, have earlier been successfully applied to find the parameter values for the fuel cell polarization curve models. The structure of these, typically semi-empirical, models have evolved during the decades. In this study, the model structures were reviewed and a new model structure was generated. Genetic algorithms were used to determine the optimized model structure with linear model parameters. Four different fuel cells, one with varying operating conditions, were studied. The results show that the model can outperform the semiempirical model utilized in number of studies without increasing the model complexity.

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Section: Resultsmentioning

confidence: 99%

Genetic Algorithms in Model Structure Identification for Fuel Cell Polarization Curve

Ohenoja

Sorsa

Leiviskä

2018

2018 5th International Conference on Control, Decision and Information Technologies (CoDIT)

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“…[4][5][6] The -electrolyte membranes (PEM) have sufficient properties to achieve high performance and resilience required for FCs. 7,9,10 Current can directly be produced during the normal operation between hydrogen and oxygen in the presence of platinum as reagent. 7,8 Among them, proton-exchange membrane fuel cell (PEMFC) has low operating pressure, low operating temperature, low relative humidity, high efficiency, green source, no contamination, and waste less other than other types.…”

Section: Introductionmentioning

confidence: 99%

“…The FCs can be categorized into a number of major types as presented in Liu et al and Kim and Lee. 9 It can be utilized for different power applications. 7,9,10 Current can directly be produced during the normal operation between hydrogen and oxygen in the presence of platinum as reagent.…”

Section: Introductionmentioning

confidence: 99%

“…9 It can be utilized for different power applications. 9 The mechanical modeling deals with the electrochemical, heat, and mass parameters. 7 The identification of the best utilized properties of the FC is a big challenge for the simple and accurate modeling 6 .…”

Section: Introductionmentioning

confidence: 99%

“…7,8 Among them, proton-exchange membrane fuel cell (PEMFC) has low operating pressure, low operating temperature, low relative humidity, high efficiency, green source, no contamination, and waste less other than other types. 7,9,10 Current can directly be produced during the normal operation between hydrogen and oxygen in the presence of platinum as reagent. 9 It can be utilized for different power applications.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

2019

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Summary I/V polarization curves of proton‐exchange membrane fuel cells (PEMFCs) are used to characterize the performance of single cells and stacks. Numerous semi‐empirical models are presented to predict such polarization curves by determining the unknown parameters of mathematical model of the PEMFCs stack. In this paper, a novel optimization approach, namely neural network algorithm (NNA) is applied for an estimation of the unknown PEMFC model parameters. The NNA is employed to minimize adopted objective function, which is formulated as the sum of squared deviations (SSD) between the actual data and estimated voltage points subjects to set of inequality constraints are satisfied. Three commercial types of PEMFCs stack namely Ballard Mark V, BCS‐500 W, and Nedstack PS6 are numerically simulated to show the effectiveness of the proposed NNA‐based tool for parameter identification. The minimum values of SSD are 0.8536 V2 for Ballard Mark V, 0.011698 V2 for BCS‐500 W stack, and 2.14487 V2 for Nedstack PS6, respectively. The obtained results of the NNA are compared with other optimizers recently published in the literature such as flower pollination algorithm, slap swarm optimizer, grey wolf algorithm, grasshopper optimization algorithm, and shark smell algorithm under the same conditions. The comparisons and other performance tests indicate the robustness and the competition of the adopted NNA‐based method for producing accurate I/V polarization curves under different operating scenarios.

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Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

2019

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Summary This study proposes precise modeling for the proton exchange membrane fuel cells which present desirable advantages compared with other energy management systems. The presented model can be applied for the simulation of the actual behavior of the proton exchange membrane fuel cells such as the electrical, electrochemical, and mechanical. In the present literature, a newly presented optimizer namely Satin Bowerbird is implemented for the evaluation of the proton exchange membrane fuel cell performance criteria. The Satin Bowerbird optimizer is an evolutionary algorithm that imitates the mating process of the Bowerbirds in the mating season. The Satin Bowerbird optimizer is applied to the different commercial benchmark of proton exchange membrane fuel cell stack to assess the performance of the proposed algorithm. The statistical study is also carried out to show the superiority of the proposed method compared with other schemes. The standard deviation for the Satin Bowerbird optimizer is obtained 0.0941 which is the lowest value amongst the other well‐known approaches. Also, the lowest sum of squared error is calculated for the proposed algorithm. Moreover, the validation of the presented method is done with the experimental data which shows good agreement between the experimental and modeling data.

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Modeling and optimization for proton exchange membrane fuel cell stack using aging and challenging P systems based optimization algorithm

Cited by 48 publications

References 40 publications

Genetic Algorithms in Model Structure Identification for Fuel Cell Polarization Curve

Genetic Algorithms in Model Structure Identification for Fuel Cell Polarization Curve

Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

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